CN111707792A - Pollution online monitoring and tracing system and method based on artificial intelligence - Google Patents
Pollution online monitoring and tracing system and method based on artificial intelligence Download PDFInfo
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- CN111707792A CN111707792A CN202010436013.9A CN202010436013A CN111707792A CN 111707792 A CN111707792 A CN 111707792A CN 202010436013 A CN202010436013 A CN 202010436013A CN 111707792 A CN111707792 A CN 111707792A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 103
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 24
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- 230000007613 environmental effect Effects 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 238000011835 investigation Methods 0.000 claims abstract description 9
- 238000012216 screening Methods 0.000 claims abstract description 9
- 239000010865 sewage Substances 0.000 claims abstract description 9
- 238000012795 verification Methods 0.000 claims abstract description 6
- 239000003344 environmental pollutant Substances 0.000 claims description 115
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- 229910052782 aluminium Inorganic materials 0.000 claims description 10
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims description 10
- 239000003945 anionic surfactant Substances 0.000 claims description 10
- 229910052787 antimony Inorganic materials 0.000 claims description 10
- WATWJIUSRGPENY-UHFFFAOYSA-N antimony atom Chemical compound [Sb] WATWJIUSRGPENY-UHFFFAOYSA-N 0.000 claims description 10
- 229910052788 barium Inorganic materials 0.000 claims description 10
- DSAJWYNOEDNPEQ-UHFFFAOYSA-N barium atom Chemical compound [Ba] DSAJWYNOEDNPEQ-UHFFFAOYSA-N 0.000 claims description 10
- SXDBWCPKPHAZSM-UHFFFAOYSA-M bromate Inorganic materials [O-]Br(=O)=O SXDBWCPKPHAZSM-UHFFFAOYSA-M 0.000 claims description 10
- 229910052793 cadmium Inorganic materials 0.000 claims description 10
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 claims description 10
- XTEGARKTQYYJKE-UHFFFAOYSA-M chlorate Inorganic materials [O-]Cl(=O)=O XTEGARKTQYYJKE-UHFFFAOYSA-M 0.000 claims description 10
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- 229910052802 copper Inorganic materials 0.000 claims description 10
- 239000010949 copper Substances 0.000 claims description 10
- 229940117389 dichlorobenzene Drugs 0.000 claims description 10
- 229910001385 heavy metal Inorganic materials 0.000 claims description 10
- 229910052742 iron Inorganic materials 0.000 claims description 10
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 claims description 10
- 238000005259 measurement Methods 0.000 claims description 10
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 claims description 10
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- 229910052750 molybdenum Inorganic materials 0.000 claims description 10
- 239000011733 molybdenum Substances 0.000 claims description 10
- 229910052759 nickel Inorganic materials 0.000 claims description 10
- 229910052758 niobium Inorganic materials 0.000 claims description 10
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- GUCVJGMIXFAOAE-UHFFFAOYSA-N niobium atom Chemical compound [Nb] GUCVJGMIXFAOAE-UHFFFAOYSA-N 0.000 claims description 10
- 239000002957 persistent organic pollutant Substances 0.000 claims description 10
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 claims description 10
- 229910052709 silver Inorganic materials 0.000 claims description 10
- 239000004332 silver Substances 0.000 claims description 10
- 229910052718 tin Inorganic materials 0.000 claims description 10
- 229910052720 vanadium Inorganic materials 0.000 claims description 10
- LEONUFNNVUYDNQ-UHFFFAOYSA-N vanadium atom Chemical compound [V] LEONUFNNVUYDNQ-UHFFFAOYSA-N 0.000 claims description 10
- 239000008096 xylene Substances 0.000 claims description 10
- 229910052725 zinc Inorganic materials 0.000 claims description 10
- 239000011701 zinc Substances 0.000 claims description 10
- 150000002632 lipids Chemical class 0.000 claims description 9
- 150000001450 anions Chemical class 0.000 claims description 6
- 229910002651 NO3 Inorganic materials 0.000 claims description 5
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 claims description 5
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 5
- 229940006460 bromide ion Drugs 0.000 claims description 5
- 229940005989 chlorate ion Drugs 0.000 claims description 5
- 238000002474 experimental method Methods 0.000 claims description 5
- 238000007689 inspection Methods 0.000 claims description 5
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- 238000012797 qualification Methods 0.000 claims description 5
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- KRHYYFGTRYWZRS-UHFFFAOYSA-M Fluoride anion Chemical compound [F-] KRHYYFGTRYWZRS-UHFFFAOYSA-M 0.000 claims description 4
- 125000000129 anionic group Chemical group 0.000 claims description 4
- YCKRFDGAMUMZLT-UHFFFAOYSA-N Fluorine atom Chemical compound [F] YCKRFDGAMUMZLT-UHFFFAOYSA-N 0.000 claims 1
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Abstract
The invention discloses an artificial intelligence-based pollution online monitoring and tracing system and method, and belongs to the field of environmental protection. Establishing a chemical fingerprint library of the polluted wastewater; monitoring on line and collecting pollution risk data in real time; accident risk characterization and screening; quantitatively analyzing and comparing data to determine the possibility of accident occurrence; five steps of field investigation, evidence obtaining and verification are carried out when accident occurrence is possible. The invention establishes a sewage discharge fingerprint database by performing a background investigation on the sewage discharge condition of a factory in a target water area, then determines the uncertainty between the monitored data and the actual data by an inversion method, and then determines the place where the pollution source occurs by comparing the similarity and the uncertainty. The problems of subjectivity, hysteresis, high equipment requirement and large workload of the traditional pollution accident pollution source tracing method are solved, the difficulty of data detection is reduced, and the feasibility of the tracing method is improved.
Description
Technical Field
The invention belongs to the field of environmental protection, and particularly relates to an artificial intelligence-based pollution online monitoring and tracing system and method.
Background
As a typical developing country, water areas such as rivers and the like in China are always threatened by chemical leakage accidents, and water pollution events caused by night stealing and draining behaviors of a small part of factories often occur. Other developing countries and developed countries also face similar puzzles and challenges to the occurrence of the same river pollution events, and therefore, the premise needs to be developed for tracing pollution sources based on monitoring data and determining pollution emission information to carry out emergency treatment and risk prevention and control.
At present, a relatively effective pollution source tracing method generally utilizes isotope analysis, infrared spectroscopy and other high-precision equipment to perform chemical characteristic analysis so as to realize the investigation of a risk source, but the method is more suitable for one type of characteristic pollutants. For the water body pollution of a small watershed, as the domestic local environmental protection department does not have the capability of isotope analysis and infrared spectrum at all, a sample needs to be sent to a three-party detection mechanism for quantitative detection, and then data is further analyzed and processed without timeliness, so that relevant evidence can be obtained in time, and the practical feasibility is lacked. In addition, more empirical data is used to infer the correlation between the accident chemical and the risk source contaminant, and no specifications or standards have been developed. At present, at home and abroad environmental protection departments at home more rely on empirical methods to check accident suspected risk sources one by one, so how to scientifically, reasonably and feasible check the accident suspected risk sources through artificial intelligence, big data and other modes becomes a problem which needs to be solved urgently by the home and local environmental protection departments.
Disclosure of Invention
The purpose of the invention is as follows: the pollution online monitoring and tracing system and method based on artificial intelligence are provided to solve the problems related to the background.
The technical scheme is as follows: an artificial intelligence based pollution on-line monitoring and tracing system and method includes the following steps:
step one, establishing a chemical fingerprint library of polluted wastewater;
secondly, online monitoring and real-time collection of pollution risk data;
step three, accident risk qualification and screening;
step four, quantitatively analyzing and comparing data to determine the possibility of accident occurrence;
and step five, carrying out on-site investigation, evidence obtaining and verification of accident occurrence possibility.
As a preferred scheme, the establishing of the risk chemical fingerprint database comprises the following steps:
s11, recording and monitoring coordinates of all factory sewage outlets and receiving water samples in the water source based on artificial intelligence;
s12, obtaining, establishing and updating the receiving water body sample through regular factory reporting and irregular field inspection;
s13, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in the polluted wastewater during discharge as an index to form an 18-dimensional vector
；
S14, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting 8 stable anionic pollutants of fluoride ions, chloride ions, bromide ions, chlorate ions, bromate ions, nitrate ions, phosphate ions and sulfate ions to occupy the average concentration of the polluted wastewater when discharged as indexes, and forming an 8-dimensional vector
；
S15, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organic phosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid in the polluted wastewater during discharge as an index to form a 15-dimensional vector
；
S16, and establishing a chemical fingerprint library of the polluted wastewater by taking three vectors from S13 to S15 as basic indexes.
As a preferred scheme, the online monitoring and real-time collection of pollution risk data comprises the following steps: the pollutants in the water area are detected on line at a preset frequency through fixed monitoring points arranged at preset intervals, and after the pollutants are determined to exist, the water area at the preset position is detected on line and collected in real time by adopting a mobile artificial intelligence on-line detection platform.
As a preferred solution, the accident risk characterization and screening comprises the following steps:
s31, determining the occurrence of a pollutant event, wherein a fault in the water area detects that the pollutant exceeds the environmental protection requirement or a plurality of pollutants exceed the background index of the water area simultaneously;
s32, if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximately normal distribution, the pollutant can be regarded as instantaneous emission; if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximate to a straight line, continuous discharge can be considered;
s33, quantitatively analyzing the pollutants in the water area, taking a water area sample sampled by a monitoring point which detects the risk of the pollutants at the first time as a first sample, taking a water area sample sampled by a monitoring point at the upstream of the water area sample as a second sample, and quantitatively analyzing the specific components of the first sample and the second sample:
selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in a first sample as an index to form an 18-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 18-dimensional vector
Selecting the average concentration of 8 stable anions of fluoride ion, chloride ion, bromide ion, chlorate ion, bromate ion, nitrate ion, phosphate ion and sulfate ion in the first sample as an index to form an 8-dimensional vector
(ii) a Account for the second sampleIs used as an index to form an 8-dimensional vector
；
Selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organophosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid occupying a first sample when being discharged as an index to form a 15-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form a 15-dimensional vector
；
S34, calculating uncertainty and introducing system uncertainty
Wherein, in the step (A),
an uncertainty component caused by the diffusion of contaminants in the water;
undetermined components caused by sampling of the monitoring points;
uncertainty components caused by experimental errors are related to an experimental method;
is an uncertainty component caused in the factory production process; k is the degradation rate;
s35, comparing the similarity measurement between the water area pollutant composition of the monitoring point and the database composition, when the following conditions are satisfied,
i.e. may be considered as a possible risk point.
Preferably, the component of uncertainty caused by the diffusion of said contaminant in the body of water
The calculation method of (2) is as follows:
wherein the content of the first and second substances,
in order to calculate theoretical pollutant concentration data according to a one-dimensional river pollutant diffusion model,
detecting data for the concentration of the contaminant at the ith sampling point;
obey normal distribution
。
Preferably, the undetermined component caused by sampling of the monitoring point
The calculation method of (2) is as follows: the concentration difference of pollutants obtained by sampling different sections of a water area by the same monitoring point is mainly caused by the uncertainty of random sampling, so that
Wherein the content of the first and second substances,
detecting data for the concentration of the contaminant at the ith sampling point;
is the average concentration of the contaminant over multiple measurements; meanwhile, multiple sampling can be performed, the sampling interval is expanded, and partial unreliable data are rejected to reduce
Specifically, when the sampling interval is greater than 0.5 m and the number of samples is greater than 12, the uncertainty component caused by sampling at the monitoring point
Less than 1.0%.
As a preferred scheme, the pollutant instantaneous discharge one-dimensional river pollutant diffusion model is as follows:
wherein the content of the first and second substances,
the total pollutant emission amount can be obtained by utilizing the inversion of the distance between monitoring points;
、
the diffusion coefficients of pollutants in the water body in the longitudinal direction and the transverse direction are shown;
、
the average flow speed in the longitudinal direction and the average flow speed of the transverse water flow of the one-dimensional water area are obtained;
the time spent from the pollutant discharge to the monitoring point is the time spent;
、
the longitudinal distance and the transverse distance from the monitoring point to the risk point are set; k is the degradation rate;
as a preferred scheme, the one-dimensional river pollutant diffusion model for pollutant continuous discharge comprises the following steps:
wherein the content of the first and second substances,
the pollutant discharge flow can be obtained by utilizing the inversion of the distance between monitoring points;
、
the diffusion coefficients of pollutants in the water body in the longitudinal direction and the transverse direction are shown;
the longitudinal flow velocity of the water flow;
、
the longitudinal distance and the transverse distance from the monitoring point to the risk point are set; k is the degradation rate.
The invention also provides an artificial intelligence-based pollution online monitoring and tracing system, which is characterized by comprising the following components:
the first module is used for establishing a chemical fingerprint library of the polluted wastewater;
the second module is used for monitoring on line and collecting pollution risk data in real time;
the third module is used for accident risk qualification and screening;
the fourth module is used for quantitatively analyzing and comparing data to determine the possibility of accident occurrence;
and the fifth module is used for carrying out on-site investigation, evidence obtaining and verification of accident occurrence possibility.
As a preferred embodiment, the first module is produced by
S11, recording and monitoring coordinates of all factory sewage outlets and receiving water samples in the water source based on artificial intelligence;
s12, obtaining, establishing and updating the receiving water body sample through regular factory reporting and irregular field inspection;
s13, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in the polluted wastewater during discharge as an index to form an 18-dimensional vector
；
S14, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting 8 stable anionic pollutants of fluoride ions, chloride ions, bromide ions, chlorate ions, bromate ions, nitrate ions, phosphate ions and sulfate ions to occupy the average concentration of the polluted wastewater when discharged as indexes, and forming an 8-dimensional vector
；
S15, quantitatively analyzing the polluted wastewater generated and discharged by each production line of the factory, and selecting anionic surfactant, cyanide, sulfide, aniline, organic phosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol, lipidThe average concentration of 15 stable organic pollutants in the polluted wastewater during discharge is used as an index to form a 15-dimensional vector
；
S16, establishing a chemical fingerprint library of the polluted wastewater by taking three vectors from S13 to S15 as basic indexes;
the second module is used for detecting pollutants in the water area on line at a preset frequency through fixed monitoring points arranged at intervals of a preset distance, and after the pollutants are determined to exist, the water area at a preset position is detected on line and collected in real time by adopting a mobile artificial intelligent on-line detection platform, so that on-line monitoring and real-time collection of pollution risk data are realized;
as a preferred embodiment, the second module is produced by
S31, determining the occurrence of a pollutant event, wherein a fault in the water area detects that the pollutant exceeds the environmental protection requirement or a plurality of pollutants exceed the background index of the water area simultaneously;
s32, if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximately normal distribution, the pollutant can be regarded as instantaneous emission; if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximate to a straight line, continuous discharge can be considered;
s33, quantitatively analyzing the pollutants in the water area, taking a water area sample sampled by a monitoring point which detects the risk of the pollutants at the first time as a first sample, taking a water area sample sampled by a monitoring point at the upstream of the water area sample as a second sample, and quantitatively analyzing the specific components of the first sample and the second sample:
selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in a first sample as an index to form an 18-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 18-dimensional directionMeasurement of
Selecting the average concentration of 8 stable anions of fluoride ion, chloride ion, bromide ion, chlorate ion, bromate ion, nitrate ion, phosphate ion and sulfate ion in the first sample as an index to form an 8-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 8-dimensional vector
；
Selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organophosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid occupying a first sample when being discharged as an index to form a 15-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form a 15-dimensional vector
；
S34, calculating uncertainty and introducing system uncertainty
Wherein, in the step (A),
an uncertainty component caused by the diffusion of contaminants in the water;
undetermined components caused by sampling of the monitoring points;
uncertainty components caused by experimental errors are related to an experimental method;
is an uncertainty component caused in the factory production process; k is the degradation rate;
s35, comparing the similarity measurement between the water area pollutant composition of the monitoring point and the database composition, when the following conditions are satisfied,
i.e. may be considered as a possible risk point.
Preferably, the component of uncertainty caused by the diffusion of said contaminant in the body of water
The calculation method of (2) is as follows:
wherein the content of the first and second substances,
in order to calculate theoretical pollutant concentration data according to a one-dimensional river pollutant diffusion model,
detecting data for the concentration of the contaminant at the ith sampling point;
obey normal distribution
；
Undetermined component caused by sampling of the monitoring points
The calculation method of (2) is as follows: the concentration difference of pollutants obtained by sampling different sections of a water area by the same monitoring point is mainly caused by the uncertainty of random sampling, so that
Wherein the content of the first and second substances,
detecting data for the concentration of the contaminant at the ith sampling point;
is the average concentration of the contaminant over multiple measurements; meanwhile, multiple sampling can be performed, the sampling interval is expanded, and partial unreliable data are rejected to reduce
Specifically, when the sampling interval is greater than 0.5 m and the number of samples is greater than 12, the uncertainty component caused by sampling at the monitoring point
Less than 1.0%.
Has the advantages that: the invention relates to an artificial intelligence-based pollution online monitoring and tracing system and method, which are characterized in that a sewage discharge fingerprint library is established by performing background investigation on the sewage discharge condition of a factory in a target water area, then the uncertainty between monitoring data and a true value is determined by an inversion method, and then the pollution source place is determined by comparing the similarity and the uncertainty. The problems of subjectivity, hysteresis, high equipment requirement and large workload of the traditional pollution accident pollution source tracing method are solved, the difficulty of data detection is reduced, and the feasibility of the tracing method is improved.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
An artificial intelligence based pollution on-line monitoring and tracing system and method includes the following steps:
step one, establishing a chemical fingerprint library of the polluted wastewater.
And step two, online monitoring and real-time collection of pollution risk data.
Step three, accident risk qualitative determination and screening.
And step four, quantitatively analyzing and comparing data to determine the possibility of accident occurrence.
And step five, carrying out on-site investigation, evidence obtaining and verification of accident occurrence possibility.
In a further embodiment, the establishing a risk chemical fingerprint library comprises the following steps:
s11, recording and monitoring coordinates of all factory sewage outlets and receiving water samples in the water source based on artificial intelligence;
s12, obtaining, establishing and updating the receiving water body sample through regular factory reporting and irregular field inspection;
s13, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in the polluted wastewater during discharge as an index to form an 18-dimensional vector
；
S14, quantitatively analyzing the polluted wastewater generated and discharged by each production line of the factory, and selecting 8 stable anions of fluoride ions, chloride ions, bromide ions, chlorate ions, bromate ions, nitrate ions, phosphate ions and sulfate ionsThe average concentration of the sub-pollutants in the polluted wastewater during discharge is used as an index to form an 8-dimensional vector
；
S15, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organic phosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid in the polluted wastewater during discharge as an index to form a 15-dimensional vector
；
S16, and establishing a chemical fingerprint library of the polluted wastewater by taking three vectors from S13 to S15 as basic indexes.
In a further embodiment, the online monitoring, real-time collection of pollution risk data comprises the steps of: the pollutants in the water area are monitored on line at a preset frequency through fixed monitoring points arranged at preset intervals, and after the pollutants are determined to exist, the water area at the preset position is monitored on line and collected in real time by adopting a mobile artificial intelligence on-line detection platform.
In a further embodiment, the accident risk characterization and screening comprises the steps of:
s31, determining the occurrence of a pollutant event, wherein a fault in the water area detects that the pollutant exceeds the environmental protection requirement or a plurality of pollutants exceed the background index of the water area simultaneously;
s32, if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximately normal distribution, the pollutant can be regarded as instantaneous emission; if the curve of the change of the pollutant concentration detected by the fixed monitoring point along with the time is approximate to a straight line, the continuous discharge can be considered.
S33, quantitatively analyzing the pollutants in the water area, taking a water area sample sampled by a monitoring point which detects the risk of the pollutants at the first time as a first sample, taking a water area sample sampled by a monitoring point at the upstream of the water area sample as a second sample, and quantitatively analyzing the specific components of the first sample and the second sample:
selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in a first sample as an index to form an 18-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 18-dimensional vector
；
Selecting the average concentration of 8 stable anions of fluoride ion, chloride ion, bromide ion, chlorate ion, bromate ion, nitrate ion, phosphate ion and sulfate ion in the first sample as an index to form an 8-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 8-dimensional vector
；
Selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organophosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid occupying a first sample when being discharged as an index to form a 15-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form a 15-dimensional vector
。
S34, calculating is not accurateDegree of certainty, introduction of system uncertainty
Wherein, in the step (A),
an uncertainty component caused by the diffusion of contaminants in the water;
undetermined components caused by sampling of the monitoring points;
uncertainty components caused by experimental errors are related to an experimental method, and no further calculation is performed;
is an uncertainty component caused in the factory production process; when a plant produces a recorded product, the proportion of the by-product is relatively stable, and only when the discharge amount of untreated wastewater is large, a pollution accident can be caused, so that the uncertainty component caused in the production process of the plant has higher stability, generally less than 3%; k is the degradation rate.
In a further embodiment, the uncertainty component caused by the diffusion of said contaminant in the water body
The calculation method of (2) is as follows:
wherein the content of the first and second substances,
in order to calculate theoretical pollutant concentration data according to a one-dimensional river pollutant diffusion model,
detecting data for the concentration of the contaminant at the ith sampling point;
obey normal distribution
。
Wherein, the pollutant instantaneous discharge one-dimensional river pollutant diffusion model:
wherein the content of the first and second substances,
the total pollutant emission amount can be obtained by utilizing the inversion of the distance between monitoring points;
、
the diffusion coefficients of pollutants in the water body in the longitudinal direction and the transverse direction are shown;
、
the average flow speed in the longitudinal direction and the average flow speed of the transverse water flow of the one-dimensional water area are obtained;
the time spent from the pollutant discharge to the monitoring point is the time spent;
、
for monitoring points from risk pointsA longitudinal distance and a transverse distance; k is the degradation rate.
As a preferred scheme, the one-dimensional river pollutant diffusion model for pollutant continuous discharge comprises the following steps:
wherein the content of the first and second substances,
the pollutant discharge flow can be obtained by utilizing the inversion of the distance between monitoring points;
、
the diffusion coefficients of pollutants in the water body in the longitudinal direction and the transverse direction are shown;
the longitudinal flow velocity of the water flow;
、
the longitudinal distance and the transverse distance from the monitoring point to the risk point are set; k is the degradation rate.
In a further embodiment, the undetermined component caused by sampling of the monitoring points
The calculation method of (2) is as follows: the concentration difference of pollutants obtained by sampling different sections of a water area by the same monitoring point is mainly caused by the uncertainty of random sampling, so that
Wherein the content of the first and second substances,
detecting data for the concentration of the contaminant at the ith sampling point;
is the average concentration of the contaminant over multiple measurements; meanwhile, multiple sampling can be performed, the sampling interval is expanded, and partial unreliable data are rejected to reduce
Specifically, when the sampling interval is greater than 0.5 m and the number of samples is greater than 12, the uncertainty component caused by sampling at the monitoring point
Less than 1.0%.
S35, comparing the similarity measurement between the water area pollutant composition of the monitoring point and the database composition, when the following conditions are satisfied,
i.e. may be considered as a possible risk point.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
Claims (10)
1. An artificial intelligence-based pollution online monitoring and tracing method is characterized by comprising the following steps:
step one, establishing a chemical fingerprint library of polluted wastewater;
secondly, online monitoring and real-time collection of pollution risk data;
step three, accident risk qualification and screening;
step four, quantitatively analyzing and comparing data to determine the possibility of accident occurrence;
and step five, carrying out on-site investigation, evidence obtaining and verification of accident occurrence possibility.
2. The pollution online monitoring and tracing method based on artificial intelligence as claimed in claim 1, wherein said establishing a risk chemical fingerprint database comprises the following steps:
s11, recording and monitoring coordinates of all factory sewage outlets and receiving water samples in the water source based on artificial intelligence;
s12, obtaining, establishing and updating the receiving water body sample through regular factory reporting and irregular field inspection;
s13, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in the polluted wastewater during discharge as an index to form an 18-dimensional vector
；
S14, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting 8 stable anionic pollutants of fluoride ions, chloride ions, bromide ions, chlorate ions, bromate ions, nitrate ions, phosphate ions and sulfate ions to occupy the average concentration of the polluted wastewater when discharged as indexes, and forming an 8-dimensional vector
；
S15, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organic phosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid in the polluted wastewater during discharge as an index to form a 15-dimensional vector
；
S16, and establishing a chemical fingerprint library of the polluted wastewater by taking three vectors from S13 to S15 as basic indexes.
3. The pollution online monitoring and tracing system and method based on artificial intelligence as claimed in claim 1, wherein said online monitoring, real-time collecting pollution risk data comprises the steps of: the pollutants in the water area are detected on line at a preset frequency through fixed monitoring points arranged at preset intervals, and after the pollutants are determined to exist, the water area at the preset position is detected on line and collected in real time by adopting a mobile artificial intelligence on-line detection platform.
4. The pollution online monitoring and tracing method based on artificial intelligence of claim 1, wherein the accident risk qualification and screening comprises the following steps:
s31, determining the occurrence of a pollutant event, wherein a fault in the water area detects that the pollutant exceeds the environmental protection requirement or a plurality of pollutants exceed the background index of the water area simultaneously;
s32, if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximately normal distribution, the pollutant can be regarded as instantaneous emission; if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximate to a straight line, continuous discharge can be considered;
s33, quantitatively analyzing the pollutants in the water area, taking a water area sample sampled by a monitoring point which detects the risk of the pollutants at the first time as a first sample, taking a water area sample sampled by a monitoring point at the upstream of the water area sample as a second sample, and quantitatively analyzing the specific components of the first sample and the second sample:
selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in the first sample as an index to form a 18-dimensional sampleVector quantity
(ii) a Taking the average concentration of the second sample as an index to form an 18-dimensional vector
Selecting the average concentration of 8 stable anions of fluoride ion, chloride ion, bromide ion, chlorate ion, bromate ion, nitrate ion, phosphate ion and sulfate ion in the first sample as an index to form an 8-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 8-dimensional vector
；
Selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organophosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid occupying a first sample when being discharged as an index to form a 15-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form a 15-dimensional vector
；
S34, calculating uncertainty and introducing system uncertainty
Wherein, in the step (A),
an uncertainty component caused by the diffusion of contaminants in the water;
undetermined components caused by sampling of the monitoring points;
uncertainty components caused by experimental errors are related to an experimental method;
is an uncertainty component caused in the factory production process; k is the degradation rate;
s35, comparing the similarity measurement between the water area pollutant composition of the monitoring point and the database composition, when the following conditions are satisfied,
i.e. may be considered as a possible risk point.
5. The artificial intelligence based pollution online monitoring and tracing method according to claim 4, wherein said pollutant has an uncertainty component caused by diffusion in water
The calculation method of (2) is as follows:
wherein the content of the first and second substances,
in order to calculate theoretical pollutant concentration data according to a one-dimensional river pollutant diffusion model,
detecting data for the concentration of the contaminant at the ith sampling point;
obey normal distribution
。
6. The artificial intelligence based pollution online monitoring and tracing method according to claim 4, wherein undetermined component caused by sampling of monitoring points
The calculation method of (2) is as follows: the concentration difference of pollutants obtained by sampling different sections of a water area by the same monitoring point is mainly caused by the uncertainty of random sampling, so that
Wherein the content of the first and second substances,
detecting data for the concentration of the contaminant at the ith sampling point;
is the average concentration of the contaminant over multiple measurements; meanwhile, multiple sampling can be performed, the sampling interval is expanded, and partial unreliable data are rejected to reduce
Specifically, when the sampling interval is greater than 0.5 m and the number of samples is greater than 12, the uncertainty component caused by sampling at the monitoring point
Less than 1.0%.
7. The pollution online monitoring and tracing method based on artificial intelligence as claimed in claim 5, wherein said pollution online monitoring and tracing method is characterized in that
One-dimensional river pollutant diffusion model for instantaneous pollutant discharge:
wherein the content of the first and second substances,
the total pollutant emission amount can be obtained by utilizing the inversion of the distance between monitoring points;
、
the diffusion coefficients of pollutants in the water body in the longitudinal direction and the transverse direction are shown;
、
the average flow speed in the longitudinal direction and the average flow speed of the transverse water flow of the one-dimensional water area are obtained;
the time spent from the pollutant discharge to the monitoring point is the time spent;
、
the longitudinal distance and the transverse distance between the monitoring point and the risk pointSeparating; k is the degradation rate;
a one-dimensional river pollutant diffusion model for pollutant continuous discharge comprises the following steps:
wherein the content of the first and second substances,
the pollutant discharge flow can be obtained by utilizing the inversion of the distance between monitoring points;
、
the diffusion coefficients of pollutants in the water body in the longitudinal direction and the transverse direction are shown;
the longitudinal flow velocity of the water flow;
、
the longitudinal distance and the transverse distance from the monitoring point to the risk point are set; k is the degradation rate.
8. The utility model provides a pollution on-line monitoring and traceability system based on artificial intelligence which characterized in that includes:
the first module is used for establishing a chemical fingerprint library of the polluted wastewater;
the second module is used for monitoring on line and collecting pollution risk data in real time;
the third module is used for accident risk qualification and screening;
the fourth module is used for quantitatively analyzing and comparing data to determine the possibility of accident occurrence;
and the fifth module is used for carrying out on-site investigation, evidence obtaining and verification of accident occurrence possibility.
9. The pollution online monitoring and tracing system based on artificial intelligence of claim 8, wherein: the first module, by
S11, recording and monitoring coordinates of all factory sewage outlets and receiving water samples in the water source based on artificial intelligence;
s12, obtaining, establishing and updating the receiving water body sample through regular factory reporting and irregular field inspection;
s13, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in the polluted wastewater during discharge as an index to form an 18-dimensional vector
；
S14, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting 8 stable anionic pollutants of fluoride ions, chloride ions, bromide ions, chlorate ions, bromate ions, nitrate ions, phosphate ions and sulfate ions to occupy the average concentration of the polluted wastewater when discharged as indexes, and forming an 8-dimensional vector
；
S15, carrying out quantitative analysis on the polluted wastewater generated and discharged by each production line of the factory, selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organic phosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid in the polluted wastewater during discharge as an index to form a 15-dimensional vector
；
S16, establishing a chemical fingerprint library of the polluted wastewater by taking three vectors from S13 to S15 as basic indexes;
the second module is used for detecting pollutants in the water area on line at a preset frequency through fixed monitoring points arranged at intervals of a preset distance, and after the pollutants are determined to exist, the water area at a preset position is detected on line and collected in real time by adopting a mobile artificial intelligent on-line detection platform, so that on-line monitoring and real-time collection of pollution risk data are realized;
the second module, by
S31, determining the occurrence of a pollutant event, wherein a fault in the water area detects that the pollutant exceeds the environmental protection requirement or a plurality of pollutants exceed the background index of the water area simultaneously;
s32, if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximately normal distribution, the pollutant can be regarded as instantaneous emission; if the time-varying curve of the pollutant concentration detected by the fixed monitoring point is approximate to a straight line, continuous discharge can be considered;
s33, quantitatively analyzing the pollutants in the water area, taking a water area sample sampled by a monitoring point which detects the risk of the pollutants at the first time as a first sample, taking a water area sample sampled by a monitoring point at the upstream of the water area sample as a second sample, and quantitatively analyzing the specific components of the first sample and the second sample:
selecting the average concentration of 18 heavy metal pollutant elements of aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, zinc, niobium, molybdenum, silver, cadmium, antimony, tin, barium, mercury and lead in a first sample as an index to form an 18-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 18-dimensional vector
Selection of fluorine ionsThe average concentration of 8 stable anions of chloride ion, bromide ion, chlorate ion, bromate ion, nitrate ion, phosphate ion and sulfate ion in the first sample is used as an index to form an 8-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form an 8-dimensional vector
；
Selecting the average concentration of 15 stable organic pollutants of anionic surfactant, cyanide, sulfide, aniline, organophosphorus, trichloromethane, carbon tetrachloride, benzene, xylene, ethylbenzene, chlorobenzene, dichlorobenzene, p-nitrochlorobenzene, phenol and lipid occupying a first sample when being discharged as an index to form a 15-dimensional vector
(ii) a Taking the average concentration of the second sample as an index to form a 15-dimensional vector
；
S34, calculating uncertainty and introducing system uncertainty
Wherein, in the step (A),
an uncertainty component caused by the diffusion of contaminants in the water;
undetermined components caused by sampling of the monitoring points;
uncertainty components caused by experimental errors are related to an experimental method;
is an uncertainty component caused in the factory production process; k is the degradation rate;
s35, comparing the similarity measurement between the water area pollutant composition of the monitoring point and the database composition, when the following conditions are satisfied,
i.e. may be considered as a possible risk point.
10. The artificial intelligence based pollution online monitoring and tracing system of claim 9, wherein said pollutant has an uncertainty component caused by diffusion in the water
The calculation method of (2) is as follows:
wherein the content of the first and second substances,
in order to calculate theoretical pollutant concentration data according to a one-dimensional river pollutant diffusion model,
detecting data for the concentration of the contaminant at the ith sampling point;
obey normal distribution
；
Undetermined component caused by sampling of the monitoring points
The calculation method of (2) is as follows: the concentration difference of pollutants obtained by sampling different sections of a water area by the same monitoring point is mainly caused by the uncertainty of random sampling, so that
Wherein the content of the first and second substances,
detecting data for the concentration of the contaminant at the ith sampling point;
is the average concentration of the contaminant over multiple measurements; meanwhile, multiple sampling can be performed, the sampling interval is expanded, and partial unreliable data are rejected to reduce
Specifically, when the sampling interval is greater than 0.5 m and the number of samples is greater than 12, the uncertainty component caused by sampling at the monitoring point
Less than 1.0%.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112520793A (en) * | 2020-09-29 | 2021-03-19 | 广州市天驰测绘技术有限公司 | Black and odorous water body electrical property tracing method |
| CN112685522A (en) * | 2020-12-25 | 2021-04-20 | 广东奥博信息产业股份有限公司 | River health management method and system |
| CN115114352A (en) * | 2022-08-25 | 2022-09-27 | 深圳市华云中盛科技股份有限公司 | Rapid analysis management system for typical pollutants |
-
2020
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112520793A (en) * | 2020-09-29 | 2021-03-19 | 广州市天驰测绘技术有限公司 | Black and odorous water body electrical property tracing method |
| CN112685522A (en) * | 2020-12-25 | 2021-04-20 | 广东奥博信息产业股份有限公司 | River health management method and system |
| CN115114352A (en) * | 2022-08-25 | 2022-09-27 | 深圳市华云中盛科技股份有限公司 | Rapid analysis management system for typical pollutants |
| CN115114352B (en) * | 2022-08-25 | 2023-01-31 | 深圳市华云中盛科技股份有限公司 | Rapid analysis and management system for typical pollutants |
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